Jacob Jackson was all-in on AI early in his career.
Jackson co-founded Tabnine, the AI coding assistant that went on to raise close to $60 million in venture backing, while still a computer science student at the University of Waterloo. After selling Tabnine to Codata in 2019 (during his final exams), Jackson joined OpenAI as an intern, where he worked until 2022.
It was at that juncture that Jackson had the urge to start a company again, one focused on supporting common developer workflows.
“In the years since I built Tabnine, tools like ChatGPT and GitHub Copilot have changed the way developers work,” Jackson said. “It’s a really exciting time to be working on developer tools because the underlying technology has improved so much since I started Tabnine — which has led to many more developers becoming interested in using AI tools to accelerate their workflow.”
So Jackson started Supermaven, an AI coding platform along the lines of Tabnine but with a few quality of life and technical upgrades.
Supermaven’s in-house generative AI model, Babble, can understand a lot of code at once, Jackson says, thanks to a 1 million-token context window. (In data science, tokens are subdivided bits of raw data — like the syllables “fan,” “tas” and “tic” in the word “fantastic.”)
A model’s context, or context window, refers to input data (e.g. code) that the model considers before generating output (e.g. additional code). Long context can prevent models from “forgetting” the content of recent docs and data, and from veering off topic and extrapolating wrongly.
“Our large context window helps reduce the frequency of hallucinations because it lets the model draw answers from the context in situations where it would otherwise have to guess,” Jackson said.
One million tokens is a big context window, indeed. But it’s not bigger than AI coding startup Magic’s, which is 100 million tokens. Meanwhile, Google’s recently introduced Code Assist tool matches Supermaven’s context at 1 million tokens.
So what are Supermaven’s advantages over rivals? Well, Jackson claims that Babble is lower-latency thanks to a “new neural architecture.” He wouldn’t elaborate beyond saying that the architecture was developed “from scratch.”
“Supermaven spends 10 to 20 seconds processing a developer’s code repository to become familiar with its APIs and the unique conventions of its codebase,” Jackson said. “With lower latency because of our in-house model serving infrastructure, our tool remains responsive while working with the long prompts that come with large codebases.”
The market for AI coding tools is a large and growing one, with Polaris Research projecting that it’ll be worth $27.17 billion by 2032. The vast majority of respondents in GitHub’s latest dev poll say that they’ve adopted AI tools in some form, and over 1.8 million people — and ~50,000 businesses — are paying for GitHub Copilot.
But Supermaven — along with startup competitors like Cognition, Anysphere, Poolside, Codeium, and Augment — have ethical and legal challenges to overcome.
Businesses are often wary of exposing proprietary code to a third party; for instance, Apple reportedly banned staff from using Copilot last year, citing concerns about confidential data leakage. Some code-generating tools trained using restrictively licensed or copyrighted code have been shown to regurgitate that code when prompted in a certain way, posing a liability risk (i.e. developers that incorporate the code could be sued). And, because AI makes mistakes, assistive coding tools can result in more mistaken and insecure code being pushed to codebases.
Jackson said that Supermaven doesn’t use customer data to train its models. He did admit, however, that the company retains data for a week to “make the system quick and responsive,” he said. On the subject of copyright, Jackson didn’t explicitly deny that Babble was trained on IP-protected code — only that it was “trained almost exclusively on publicly available code rather than a scrape of the public internet” to “reduce exposure to toxic content during training.”
Customers don’t appear to be dissuaded. More than 35,000 developers are using Supermaven, Jackson says, and a sizeable chunk are paying for the premium Pro ($10 per month) and Team ($10 per month per use) plans. Supermaven’s annual recurring revenue reached $1 million this year on the back of a user base that’s grown 3x since the platform’s February launch.
That momentum got the attention of VCs.
Supermaven this week announced its first outside funding: a $12 million round led by Bessemer Venture Partners and high-profile angel investors including OpenAI co-founder John Schulman and Perplexity co-founder Denis Yarats. Jackson says that the plan is to spend the money on hiring developers (Supermaven has a five-person team presently) and developing Supermaven’s text editor, which is currently in beta.
“We plan to grow significantly through the end of the year,” he added. “Despite headwinds for tech overall, the market for coding copilots has been growing quickly. Our growth since our launch in February — as well as our most recent funding round — position us well as we head into next year.”
